{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Topic Modeling: Financial News"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook contains an example of LDA applied to financial news articles."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Imports & Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T22:16:05.888776Z",
     "start_time": "2020-06-20T22:16:05.886691Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T22:16:07.120775Z",
     "start_time": "2020-06-20T22:16:05.890282Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from collections import Counter\n",
    "from pathlib import Path\n",
    "import logging\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# spacy for language processing\n",
    "import spacy\n",
    "\n",
    "# sklearn for feature extraction\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "# gensim for topic models\n",
    "from gensim.models import LdaModel\n",
    "from gensim.corpora import Dictionary\n",
    "from gensim.matutils import Sparse2Corpus\n",
    "\n",
    "# topic model viz\n",
    "import pyLDAvis\n",
    "from pyLDAvis.gensim import prepare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T22:16:07.123708Z",
     "start_time": "2020-06-20T22:16:07.121803Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.set_style('white')\n",
    "pyLDAvis.enable_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T22:16:07.315574Z",
     "start_time": "2020-06-20T22:16:07.124495Z"
    }
   },
   "outputs": [],
   "source": [
    "stop_words = set(pd.read_csv('http://ir.dcs.gla.ac.uk/resources/linguistic_utils/stop_words',\n",
    "                             header=None,\n",
    "                             squeeze=True).tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper Viz Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T22:16:07.343534Z",
     "start_time": "2020-06-20T22:16:07.332491Z"
    }
   },
   "outputs": [],
   "source": [
    "def show_word_list(model, corpus, top=10, save=False):\n",
    "    top_topics = model.top_topics(corpus=corpus, coherence='u_mass', topn=20)\n",
    "    words, probs = [], []\n",
    "    for top_topic, _ in top_topics:\n",
    "        words.append([t[1] for t in top_topic[:top]])\n",
    "        probs.append([t[0] for t in top_topic[:top]])\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(model.num_topics*1.2, 5))\n",
    "    sns.heatmap(pd.DataFrame(probs).T,\n",
    "                annot=pd.DataFrame(words).T,\n",
    "                fmt='',\n",
    "                ax=ax,\n",
    "                cmap='Blues',\n",
    "                cbar=False)\n",
    "    fig.tight_layout()\n",
    "    if save:\n",
    "        fig.savefig(f'fin_news_wordlist_{top}', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T22:16:07.354134Z",
     "start_time": "2020-06-20T22:16:07.345563Z"
    }
   },
   "outputs": [],
   "source": [
    "def show_coherence(model, corpus, tokens, top=10, cutoff=0.01):\n",
    "    top_topics = model.top_topics(corpus=corpus, coherence='u_mass', topn=20)\n",
    "    word_lists = pd.DataFrame(model.get_topics().T, index=tokens)\n",
    "    order = []\n",
    "    for w, word_list in word_lists.items():\n",
    "        target = set(word_list.nlargest(top).index)\n",
    "        for t, (top_topic, _) in enumerate(top_topics):\n",
    "            if target == set([t[1] for t in top_topic[:top]]):\n",
    "                order.append(t)\n",
    "\n",
    "    fig, axes = plt.subplots(ncols=2, figsize=(15,5))\n",
    "    title = f'# Words with Probability > {cutoff:.2%}'\n",
    "    (word_lists.loc[:, order]>cutoff).sum().reset_index(drop=True).plot.bar(title=title, ax=axes[1]);\n",
    "\n",
    "    umass = model.top_topics(corpus=corpus, coherence='u_mass', topn=20)\n",
    "    pd.Series([c[1] for c in umass]).plot.bar(title='Topic Coherence', ax=axes[0])\n",
    "    fig.tight_layout()\n",
    "    fig.savefig(f'fin_news_coherence_{top}', dpi=300);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T22:16:07.376792Z",
     "start_time": "2020-06-20T22:16:07.355235Z"
    }
   },
   "outputs": [],
   "source": [
    "def show_top_docs(model, corpus, docs):\n",
    "    doc_topics = model.get_document_topics(corpus)\n",
    "    df = pd.concat([pd.DataFrame(doc_topic, \n",
    "                                 columns=['topicid', 'weight']).assign(doc=i) \n",
    "                    for i, doc_topic in enumerate(doc_topics)])\n",
    "\n",
    "    for topicid, data in df.groupby('topicid'):\n",
    "        print(topicid, docs[int(data.sort_values('weight', ascending=False).iloc[0].doc)])\n",
    "        print(pd.DataFrame(lda.show_topic(topicid=topicid)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "## Load Financial News"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is avaialble from [Kaggle](https://www.kaggle.com/jeet2016/us-financial-news-articles). \n",
    "\n",
    "Download and unzip into data directory in repository root folder, then rename the enclosing folder to `us-financial-news` and the subfolders so you get the following directory structure:\n",
    "```\n",
    "data\n",
    "  |-us-financial-news\n",
    "     |-2018_01\n",
    "     |-2018_02\n",
    "     |-2018_03\n",
    "     |-2018_04\n",
    "     |-2018_05\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = Path('..', 'data', 'us-financial-news')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We limit the article selection to the following sections in the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T22:16:07.384782Z",
     "start_time": "2020-06-20T22:16:07.378004Z"
    }
   },
   "outputs": [],
   "source": [
    "section_titles = ['Press Releases - CNBC',\n",
    "                  'Reuters: Company News',\n",
    "                  'Reuters: World News',\n",
    "                  'Reuters: Business News',\n",
    "                  'Reuters: Financial Services and Real Estate',\n",
    "                  'Top News and Analysis (pro)',\n",
    "                  'Reuters: Top News',\n",
    "                  'The Wall Street Journal &amp; Breaking News, Business, Financial and Economic News, World News and Video',\n",
    "                  'Business &amp; Financial News, U.S &amp; International Breaking News | Reuters',\n",
    "                  'Reuters: Money News',\n",
    "                  'Reuters: Technology News']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T22:16:07.395809Z",
     "start_time": "2020-06-20T22:16:07.385999Z"
    }
   },
   "outputs": [],
   "source": [
    "def read_articles():\n",
    "    articles = []\n",
    "    counter = Counter()\n",
    "    for f in data_path.glob('*/**/*.json'):\n",
    "        article = json.load(f.open())\n",
    "        if article['thread']['section_title'] in set(section_titles):\n",
    "            text = article['text'].lower().split()\n",
    "            counter.update(text)\n",
    "            articles.append(' '.join([t for t in text if t not in stop_words]))\n",
    "    return articles, counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T22:17:20.777743Z",
     "start_time": "2020-06-20T22:16:07.396629Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done loading 125,964 articles\n"
     ]
    }
   ],
   "source": [
    "articles, counter = read_articles()\n",
    "print(f'Done loading {len(articles):,.0f} articles')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T22:17:22.349934Z",
     "start_time": "2020-06-20T22:17:20.778755Z"
    }
   },
   "outputs": [],
   "source": [
    "most_common = (pd.DataFrame(counter.most_common(), columns=['token', 'count'])\n",
    "               .pipe(lambda x: x[~x.token.str.lower().isin(stop_words)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T22:17:22.372072Z",
     "start_time": "2020-06-20T22:17:22.350815Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>token</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>$</td>\n",
       "      <td>592548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>net</td>\n",
       "      <td>317060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>)</td>\n",
       "      <td>315742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>million</td>\n",
       "      <td>269721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-</td>\n",
       "      <td>258943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>income</td>\n",
       "      <td>257158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2018</td>\n",
       "      <td>227890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>said</td>\n",
       "      <td>217339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>%</td>\n",
       "      <td>214997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>company</td>\n",
       "      <td>213316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      token   count\n",
       "8         $  592548\n",
       "17      net  317060\n",
       "18        )  315742\n",
       "23  million  269721\n",
       "25        -  258943\n",
       "26   income  257158\n",
       "31     2018  227890\n",
       "32     said  217339\n",
       "33        %  214997\n",
       "34  company  213316"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_common.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing with SpaCy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T23:11:11.141847Z",
     "start_time": "2020-06-20T23:11:11.139719Z"
    }
   },
   "outputs": [],
   "source": [
    "results_path = Path('results', 'financial_news')\n",
    "if not results_path.exists():\n",
    "    results_path.mkdir(parents=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T22:17:22.378076Z",
     "start_time": "2020-06-20T22:17:22.372858Z"
    }
   },
   "outputs": [],
   "source": [
    "def clean_doc(d):\n",
    "    doc = []\n",
    "    for t in d:\n",
    "        if not any([t.is_stop, t.is_digit, not t.is_alpha, t.is_punct, t.is_space, t.lemma_ == '-PRON-']):        \n",
    "            doc.append(t.lemma_)\n",
    "    return ' '.join(doc)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T22:17:22.910449Z",
     "start_time": "2020-06-20T22:17:22.378984Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('ner', <spacy.pipeline.pipes.EntityRecognizer at 0x7f7b43da6fa0>)]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlp = spacy.load('en')\n",
    "nlp.max_length = 6000000\n",
    "nlp.disable_pipes('ner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T22:17:22.914505Z",
     "start_time": "2020-06-20T22:17:22.911553Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tagger', 'parser']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlp.pipe_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T22:17:22.923199Z",
     "start_time": "2020-06-20T22:17:22.917962Z"
    }
   },
   "outputs": [],
   "source": [
    "def preprocess(articles):\n",
    "    iter_articles = (article for article in articles)\n",
    "    clean_articles = []\n",
    "    for i, doc in enumerate(nlp.pipe(iter_articles, \n",
    "                                     batch_size=100, \n",
    "                                     n_threads=8), 1):\n",
    "        if i % 1000 == 0:\n",
    "            print(f'{i / len(articles):.2%}', end=' ', flush=True)\n",
    "        clean_articles.append(clean_doc(doc))\n",
    "    return clean_articles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T23:09:12.062927Z",
     "start_time": "2020-06-20T22:17:22.924393Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/stefan/.pyenv/versions/miniconda3-latest/envs/ml4t/lib/python3.7/site-packages/spacy/language.py:771: DeprecationWarning: [W016] The keyword argument `n_threads` is now deprecated. As of v2.2.2, the argument `n_process` controls parallel inference via multiprocessing.\n",
      "  warnings.warn(Warnings.W016, DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.79% 1.59% 2.38% 3.18% 3.97% 4.76% 5.56% 6.35% 7.14% 7.94% 8.73% 9.53% 10.32% 11.11% 11.91% 12.70% 13.50% 14.29% 15.08% 15.88% 16.67% 17.47% 18.26% 19.05% 19.85% 20.64% 21.43% 22.23% 23.02% 23.82% 24.61% 25.40% 26.20% 26.99% 27.79% 28.58% 29.37% 30.17% 30.96% 31.76% 32.55% 33.34% 34.14% 34.93% 35.72% 36.52% 37.31% 38.11% 38.90% 39.69% 40.49% 41.28% 42.08% 42.87% 43.66% 44.46% 45.25% 46.04% 46.84% 47.63% 48.43% 49.22% 50.01% 50.81% 51.60% 52.40% 53.19% 53.98% 54.78% 55.57% 56.37% 57.16% 57.95% 58.75% 59.54% 60.33% 61.13% 61.92% 62.72% 63.51% 64.30% 65.10% 65.89% 66.69% 67.48% 68.27% 69.07% 69.86% 70.66% 71.45% 72.24% 73.04% 73.83% 74.62% 75.42% 76.21% 77.01% 77.80% 78.59% 79.39% 80.18% 80.98% 81.77% 82.56% 83.36% 84.15% 84.94% 85.74% 86.53% 87.33% 88.12% 88.91% 89.71% 90.50% 91.30% 92.09% 92.88% 93.68% 94.47% 95.27% 96.06% 96.85% 97.65% 98.44% 99.23% "
     ]
    }
   ],
   "source": [
    "clean_articles = preprocess(articles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T23:09:12.747290Z",
     "start_time": "2020-06-20T23:09:12.066156Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "333868354"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_path = results_path / 'clean_text'\n",
    "clean_path.write_text('\\n'.join(clean_articles))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Vectorize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T23:09:14.208881Z",
     "start_time": "2020-06-20T23:09:12.748258Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "125964"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs = clean_path.read_text().split('\\n')\n",
    "len(docs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore cleaned data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T23:09:19.998135Z",
     "start_time": "2020-06-20T23:09:14.209851Z"
    }
   },
   "outputs": [],
   "source": [
    "article_length, token_count = [], Counter()\n",
    "for i, doc in enumerate(docs, 1):\n",
    "    if i % 1e6 == 0:\n",
    "        print(i, end=' ', flush=True)\n",
    "    d = doc.lower().split()\n",
    "    article_length.append(len(d))\n",
    "    token_count.update(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T23:11:16.802364Z",
     "start_time": "2020-06-20T23:11:15.313515Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, figsize=(15, 5))\n",
    "(pd.DataFrame(token_count.most_common(), columns=['token', 'count'])\n",
    " .pipe(lambda x: x[~x.token.str.lower().isin(stop_words)])\n",
    " .set_index('token')\n",
    " .squeeze()\n",
    " .iloc[:25]\n",
    " .sort_values()\n",
    " .plot\n",
    " .barh(ax=axes[0], title='Most frequent tokens'))\n",
    "sns.boxenplot(x=pd.Series(article_length), ax=axes[1])\n",
    "axes[1].set_xscale('log')\n",
    "axes[1].set_xlabel('Word Count (log scale)')\n",
    "axes[1].set_title('Article Length Distribution')\n",
    "sns.despine()\n",
    "fig.tight_layout()\n",
    "fig.savefig(results_path / 'fn_explore', dpi=300);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T23:11:20.568467Z",
     "start_time": "2020-06-20T23:11:20.508734Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    125964.000000\n",
       "mean        354.514091\n",
       "std         534.782734\n",
       "min           1.000000\n",
       "10%          48.000000\n",
       "20%          85.000000\n",
       "30%         135.000000\n",
       "40%         180.000000\n",
       "50%         225.000000\n",
       "60%         267.000000\n",
       "70%         324.000000\n",
       "80%         413.000000\n",
       "90%         622.000000\n",
       "max       17838.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(article_length).describe(percentiles=np.arange(.1, 1.0, .1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T23:11:20.963934Z",
     "start_time": "2020-06-20T23:11:20.655796Z"
    }
   },
   "outputs": [],
   "source": [
    "docs = [x.lower() for x in docs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T23:11:22.236045Z",
     "start_time": "2020-06-20T23:11:22.233692Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'washington reuters treasury secretary steven mnuchin tuesday say want know consumer financial protection bureau handling probe hack credit bureau equifax report agency acting director pull investigate matter equifax disclose september hacker steal personal datum collect million americans monday reuters report act cfpb chief mick mulvaney brakes agency equifax investigation speak director mulvaney mnuchin tell house representatives financial services committee go discuss tuesday cfpb say examine equifax breach decline detail bureau look equifax datum breach response agency say statement reuters cite people familiar matter report monday cfpb open investigation equifax mulvaney rein work begin predecessor richard cordray mulvaney order subpoenas equifax seek swear testimony executive routine step scale probe source say add cfpb shelve plan ground test equifax protect datum idea back cordray cfpb recently rebuff bank regulator federal reserve federal deposit insurance corp office comptroller currency offer help site exam credit bureau source say republican president donald trump administration seek curb power cfpb create democratic predecessor barack obama protect consumer financial industry abuse agency criticize fiercely industry mulvaney seek new operating fund agency opt instead finance slimme budget shrink reserve fund establish cordray report patrick rucker lindsay dunsmuir editing franklin paul'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set vocab parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T23:11:33.777973Z",
     "start_time": "2020-06-20T23:11:33.772977Z"
    }
   },
   "outputs": [],
   "source": [
    "min_df = .005\n",
    "max_df = .1\n",
    "ngram_range = (1, 1)\n",
    "binary = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T23:11:55.784790Z",
     "start_time": "2020-06-20T23:11:33.956834Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(125964, 3736)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer = TfidfVectorizer(stop_words='english',\n",
    "                             min_df=min_df,\n",
    "                             max_df=max_df,\n",
    "                             ngram_range=ngram_range,\n",
    "                             binary=binary)\n",
    "dtm = vectorizer.fit_transform(docs)\n",
    "tokens = vectorizer.get_feature_names()\n",
    "dtm.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T23:12:03.812347Z",
     "start_time": "2020-06-20T23:11:55.786071Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "corpus = Sparse2Corpus(dtm, documents_columns=False)\n",
    "id2word = pd.Series(tokens).to_dict()\n",
    "dictionary = Dictionary.from_corpus(corpus, id2word)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train & Evaluate LDA Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T23:12:03.816150Z",
     "start_time": "2020-06-20T23:12:03.813394Z"
    }
   },
   "outputs": [],
   "source": [
    "logging.basicConfig(filename='gensim.log',\n",
    "                    format=\"%(asctime)s:%(levelname)s:%(message)s\",\n",
    "                    level=logging.DEBUG)\n",
    "logging.root.level = logging.DEBUG"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train models with 5-25 topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T23:12:03.829923Z",
     "start_time": "2020-06-20T23:12:03.817505Z"
    }
   },
   "outputs": [],
   "source": [
    "num_topics = [5, 10, 15, 20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T03:13:54.002367Z",
     "start_time": "2020-06-21T01:56:30.233284Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "10\n",
      "15\n",
      "20\n",
      "25\n"
     ]
    }
   ],
   "source": [
    "for topics in num_topics:\n",
    "    print(topics)\n",
    "    lda_model = LdaModel(corpus=corpus,\n",
    "                     id2word=id2word,\n",
    "                     num_topics=topics,\n",
    "                     chunksize=len(docs),\n",
    "                     update_every=1,\n",
    "                     alpha='auto',                     # a-priori belief for the each topics' probability\n",
    "                     eta='auto',                       # a-priori belief on word probability\n",
    "                     decay=0.5,                        # percentage of previous lambda value forgotten\n",
    "                     offset=1.0,\n",
    "                     eval_every=1,\n",
    "                     passes=10,\n",
    "                     iterations=50,\n",
    "                     gamma_threshold=0.001,\n",
    "                     minimum_probability=0.01,         # filter topics with lower probability\n",
    "                     minimum_phi_value=0.01,           # lower bound on term probabilities\n",
    "                     random_state=42)\n",
    "    lda_model.save((results_path / f'model_{topics}').as_posix())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We show results for one model using a vocabulary of 3,800 tokens based on min_df=0.1% and max_df=25% with a single pass to avoid length training time for 20 topics. We can use pyldavis topic_info attribute to compute relevance values for lambda=0.6 that produces the following word list "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T03:13:54.007584Z",
     "start_time": "2020-06-21T03:13:54.003312Z"
    }
   },
   "outputs": [],
   "source": [
    "def eval_lda_model(ntopics, model, corpus=corpus, tokens=tokens):\n",
    "    show_word_list(model=model, corpus=corpus, top=ntopics, save=True)\n",
    "    show_coherence(model=model, corpus=corpus, tokens=tokens, top=ntopics)\n",
    "    vis = prepare(model, corpus, dictionary, mds='tsne')\n",
    "    pyLDAvis.save_html(vis, f'lda_{ntopics}.html')\n",
    "    return 2 ** (-model.log_perplexity(corpus))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T03:34:49.119925Z",
     "start_time": "2020-06-21T03:19:40.931688Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "lda_models = {}\n",
    "perplexity ={}\n",
    "for ntopics in num_topics:\n",
    "    print(ntopics)\n",
    "    lda_models[ntopics] = LdaModel.load((results_path / f'model_{ntopics}').as_posix())\n",
    "    perplexity[ntopics] = eval_lda_model(ntopics=ntopics, model=lda_models[ntopics])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perplexity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T04:21:23.691981Z",
     "start_time": "2020-06-21T04:21:23.522467Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAD6CAYAAABebNdxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAPZElEQVR4nO3df2hVhf/H8dfd3e6se7cUhKx0/sphm0xXQ/8YDqTGVctsMi/txl24ETgK3RB/tKYupm4grj/EJQwhmn1aKwmEb0G4jGGOUQO3vGMVI41EREPy3mvfO/3ufv/5fmdrtem622Xvng8QvOeee8/7Du5zx3vOuTpisVhMAAATkhI9AAAgfog6ABhC1AHAEKIOAIYQdQAwhKgDgCEJjXp5eXkiNw8A5iQ06jdv3kzk5gHAHD5+AQBDiDoAGELUAcAQog4AhhB1ADCEqAOAIUQdAAwh6gBgCFEHMC39953/SfQI92Wq50ye0q0BQJzMSHFqwZ7/SvQY47rU8PyUbo89dQAwhKgDgCFEHQAMIeoAYAhRBwBDiDoAGELUgSnCedWYCpynDkwRzqvGVGBPHQAMIeoAYAhRBwBDiDoAGELUAcAQog4AhhB1jGm6nLM8XeYEJhvnqWNMnFsNTC/sqQOAIWPuqd+5c0fV1dW6cuWKBgcHVVFRoTlz5mjr1q1asGCBJKmkpETr169XW1ubWltblZycrIqKCq1Zs2Yq5gcA/MGYUT99+rRmzpypw4cP6+bNmyoqKtLrr7+uLVu2qKysbHi969evq6WlRadOnVI0GpXf71d+fr5cLtekvwAAwD1jRn3t2rXyer3Dt51Opy5evKiffvpJ7e3tmj9/vqqrq9Xb26vc3Fy5XC65XC5lZGSov79fOTk5k/4CAAD3jBl1t9stSQqHw9q2bZsqKys1ODiozZs3a9myZXr33Xd17NgxLV26VGlpaSMeFw6HJ3dyAMAo4x4ovXr1qkpLS7Vx40Zt2LBBhYWFWrZsmSSpsLBQfX198ng8ikQiw4+JRCIjIg8AmBpjRv3GjRsqKyvTzp07VVxcLEkqLy9Xb2+vJKmzs1PZ2dnKyclRd3e3otGoQqGQBgYGlJmZOfnTAwBGGPPjl+PHj+vWrVtqampSU1OTJGnPnj06dOiQUlJSNHv2bNXV1cnj8SgQCMjv9ysWi6mqqkqpqalT8gIAAPeMGfWamhrV1NSMWt7a2jpqmc/nk8/ni99kAIAHZu7io+lyufh0mRPA9GLuawK4rB3Av5m5PXUA+Dcj6gBgCFEHAEOIOgAYQtQBwBCiDgCGEHUAMISoA4AhRB0ADCHqAGAIUQcAQ4g6ABhC1AHAEKIOAIYQdQAwhKgDgCFEHQAMIeoAYAhRBwBDiDoAGELUAcAQog4AhhB1ADCEqAOAIUQdAAwh6gBgCFEHAEOSx7rzzp07qq6u1pUrVzQ4OKiKigo9+eST2rNnjxwOh5YsWaL9+/crKSlJbW1tam1tVXJysioqKrRmzZqpeg0AgP8zZtRPnz6tmTNn6vDhw7p586aKioq0dOlSVVZWatWqVdq3b5/a29u1YsUKtbS06NSpU4pGo/L7/crPz5fL5Zqq1wEA0DhRX7t2rbxe7/Btp9OpYDColStXSpIKCgr09ddfKykpSbm5uXK5XHK5XMrIyFB/f79ycnImd3oAwAhjfqbudrvl8XgUDoe1bds2VVZWKhaLyeFwDN8fCoUUDoeVlpY24nHhcHhyJwcAjDLugdKrV6+qtLRUGzdu1IYNG5SUdO8hkUhE6enp8ng8ikQiI5b/MfIAgKkxZtRv3LihsrIy7dy5U8XFxZKkrKwsdXV1SZI6OjqUl5ennJwcdXd3KxqNKhQKaWBgQJmZmZM/PQBghDE/Uz9+/Lhu3bqlpqYmNTU1SZLeeustHThwQI2NjVq0aJG8Xq+cTqcCgYD8fr9isZiqqqqUmpo6JS8AAHDPmFGvqalRTU3NqOUnT54ctczn88nn88VvMgDAA+PiIwAwhKgDgCFEHQAMIeoAYAhRBwBDiDoAGELUAcAQog4AhhB1ADCEqAOAIUQdAAwh6gBgCFEHAEOIOgAYQtQBwBCiDgCGEHUAMISoA4AhRB0ADCHqAGAIUQcAQ4g6ABhC1AHAEKIOAIYQdQAwhKgDgCFEHQAMIeoAYAhRBwBD7ivqPT09CgQCkqRgMKjVq1crEAgoEAjos88+kyS1tbVp06ZN8vl8Onv27ORNDAD4W8njrdDc3KzTp0/roYcekiT19fVpy5YtKisrG17n+vXramlp0alTpxSNRuX3+5Wfny+XyzV5kwMARhl3Tz0jI0NHjx4dvn3x4kV99dVXeuWVV1RdXa1wOKze3l7l5ubK5XIpLS1NGRkZ6u/vn9TBAQCjjRt1r9er5OR7O/Q5OTnatWuXPvjgA82bN0/Hjh1TOBxWWlra8Dput1vhcHhyJgYA/K0HPlBaWFioZcuWDf+9r69PHo9HkUhkeJ1IJDIi8gCAqfHAUS8vL1dvb68kqbOzU9nZ2crJyVF3d7ei0ahCoZAGBgaUmZkZ92EBAGMb90Dpn9XW1qqurk4pKSmaPXu26urq5PF4FAgE5Pf7FYvFVFVVpdTU1MmYFwAwhvuK+ty5c9XW1iZJys7OVmtr66h1fD6ffD5ffKcDADwQLj4CAEOIOgAYQtQBwBCiDgCGEHUAMISoA4AhRB0ADCHqAGAIUQcAQ4g6ABhC1AHAEKIOAIYQdQAwhKgDgCFEHQAMIeoAYAhRBwBDiDoAGELUAcAQog4AhhB1ADCEqAOAIUQdAAwh6gBgCFEHAEOIOgAYQtQBwBCiDgCGEHUAMOS+ot7T06NAICBJunz5skpKSuT3+7V//34NDQ1Jktra2rRp0yb5fD6dPXt28iYGAPytcaPe3NysmpoaRaNRSVJ9fb0qKyv1n//8R7FYTO3t7bp+/bpaWlrU2tqqEydOqLGxUYODg5M+PABgpHGjnpGRoaNHjw7fDgaDWrlypSSpoKBA58+fV29vr3Jzc+VyuZSWlqaMjAz19/dP3tQAgL80btS9Xq+Sk5OHb8diMTkcDkmS2+1WKBRSOBxWWlra8Dput1vhcHgSxgUAjOWBD5QmJd17SCQSUXp6ujwejyKRyIjlf4w8AGBqPHDUs7Ky1NXVJUnq6OhQXl6ecnJy1N3drWg0qlAopIGBAWVmZsZ9WADA2JLHX2Wk3bt3a+/evWpsbNSiRYvk9XrldDoVCATk9/sVi8VUVVWl1NTUyZgXADCG+4r63Llz1dbWJklauHChTp48OWodn88nn88X3+kAAA+Ei48AwBCiDgCGEHUAMISoA4AhRB0ADCHqAGAIUQcAQ4g6ABhC1AHAEKIOAIYQdQAwhKgDgCFEHQAMIeoAYAhRBwBDiDoAGELUAcAQog4AhhB1ADCEqAOAIUQdAAwh6gBgCFEHAEOIOgAYQtQBwBCiDgCGEHUAMISoA4AhRB0ADEme6ANfeuklpaWlSZLmzp2rrVu3as+ePXI4HFqyZIn279+vpCR+ZwDAVJpQ1KPRqCSppaVleNnWrVtVWVmpVatWad++fWpvb1dhYWF8pgQA3JcJ7Ur39/fr999/V1lZmUpLS3XhwgUFg0GtXLlSklRQUKDz58/HdVAAwPgmtKc+Y8YMlZeXa/Pmzbp06ZJee+01xWIxORwOSZLb7VYoFIrroACA8U0o6gsXLtT8+fPlcDi0cOFCzZw5U8FgcPj+SCSi9PT0uA0JALg/E/r45ZNPPlFDQ4Mk6dq1awqHw8rPz1dXV5ckqaOjQ3l5efGbEgBwXya0p15cXKw333xTJSUlcjgcOnTokGbNmqW9e/eqsbFRixYtktfrjfesAIBxTCjqLpdLR44cGbX85MmT/3ggAMDEcSI5ABhC1AHAEKIOAIYQdQAwhKgDgCFEHQAMIeoAYAhRBwBDiDoAGELUAcAQog4AhhB1ADCEqAOAIUQdAAwh6gBgCFEHAEOIOgAYQtQBwBCiDgCGEHUAMISoA4AhRB0ADCHqAGAIUQcAQ4g6ABhC1AHAEKIOAIYQdQAwhKgDgCHJ8XyyoaEh1dbW6vvvv5fL5dKBAwc0f/78eG4CADCGuO6pnzlzRoODg/roo4+0Y8cONTQ0xPPpAQDjiGvUu7u7tXr1aknSihUrdPHixXg+PQBgHHGNejgclsfjGb7tdDp19+7deG4CADAGRywWi8Xryerr67V8+XKtX79eklRQUKCOjo6/XX/VqlV64okn4rV5APhXmDVrlk6cOPGX98X1QOnTTz+ts2fPav369bpw4YIyMzPHXL+rqyuemweAf7247qn//9kvP/zwg2KxmA4dOqTFixfH6+kBAOOIa9QBAInFxUcAYAhRBwBDiDoAGELUxzA0NKRr165paGgo0aMAwH0h6n9SXV0tSerp6ZHX69Ubb7yhF154QRcuXEjwZAAwvriep27BL7/8Ikl655131NzcrAULFujatWvasWOHTp48meDpAMTTmTNn1NnZqVAopPT0dD3zzDNau3atHA5HokebMKL+N5xOpxYsWCBJevTRR/kI5h+w+MZJlM8//1zr1q3T7du3dfToUfX39ys7O1sVFRVyu92JHm9aefvttzU0NKSCggK53W5FIhF1dHTo3LlzOnjwYKLHmzCi/iehUEibNm3S7du39fHHH+vFF19UQ0ODHn/88USPNi1ZfeMkyocffqh169bp4MGDmjdvnmpqatTZ2al9+/bpyJEjiR5vWvnxxx9H/ev72Wef1csvv5ygieKDqP/Jp59+qsHBQfX392vGjBlyOBzKzMxUcXFxokeblqy+cRLt8uXLw78UFy9erC+++CLBE00/Q0ND+vbbb5WXlze87JtvvlFKSkoCp/rniPpfcLlcysnJGb5dUlKSwGmmN6tvnES5dOmS3nvvPSUnJ6uvr09ZWVn67rvvNDg4mOjRpp2GhgbV19drx44disViSkpK0lNPPaWamppEj/aP8DUBmFQ///yz6uvrFQwGh984WVlZ2r179/AxC9y/vr4+BYNBBYNBLV++XM8995zKy8tVW1urrKysRI83rXz55Zeqq6uT0+lUVVWVnn/+eUlSaWmp3n///QRPN3FEHcC/ks/nU3Nzs4aGhrR9+3YVFRWpqKhIgUBALS0tiR5vwvj4BZMqEAjozp07f3lfa2vrFE8z/fHzjJ+UlBQ98sgjkqSmpia9+uqreuyxx6b9WVnsqWNS9fT0qKamRseOHZPT6RxxH/9ByoPj5xk/u3bt0qxZs7R9+3Y9/PDDunr1qsrLy3Xr1i2dO3cu0eNNmLO2trY20UPArjlz5uj27du6e/euVqxYofT09OE/eHD8PONnzZo1+vXXX7VkyRKlpKQoLS1NXq9Xv/32mwoKChI93oSxpw4AhvDdLwBgCFEHAEOIOgAYQtQBwBCiDgCG/C/AaAncSYcIuwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(perplexity).plot.bar()\n",
    "sns.despine();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PyLDAVis for 15 Topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T04:23:43.663646Z",
     "start_time": "2020-06-21T04:21:45.666565Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el155951401673927676962195886734\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el155951401673927676962195886734_data = {\"mdsDat\": {\"x\": [114.34487915039062, 105.821044921875, -138.33447265625, -7.436697483062744, -207.55062866210938, 52.817649841308594, 168.01084899902344, -94.16960144042969, -122.84917449951172, -94.8942642211914, 24.927400588989258, -53.41346740722656, -12.255849838256836, -224.21795654296875, 36.15425491333008], \"y\": [114.30829620361328, -93.74962615966797, 95.32076263427734, 111.25969696044922, -105.47260284423828, 14.603764533996582, 6.806949615478516, -157.78955078125, -39.059417724609375, 195.40829467773438, -180.46487426757812, 26.832107543945312, -72.09320068359375, 28.070581436157227, 209.42758178710938], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [13.237846374511719, 9.953869819641113, 9.021284103393555, 8.353378295898438, 8.299505233764648, 8.168314933776855, 6.614908695220947, 6.251117706298828, 6.167654037475586, 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\"street\", \"street\", \"street\", \"street\", \"street\", \"street\", \"street\", \"strike\", \"strike\", \"strike\", \"strike\", \"strike\", \"strike\", \"strike\", \"strike\", \"strike\", \"strike\", \"strike\", \"strike\", \"strike\", \"strike\", \"strike\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"subordinate\", \"subordinate\", \"subordinate\", \"subordinate\", \"subordinate\", \"subordinate\", \"subordinate\", \"subordinate\", \"subordinate\", \"subscriber\", \"subscriber\", \"subscriber\", \"subscriber\", \"subscriber\", \"subscriber\", \"subscriber\", \"subscriber\", \"subscriber\", \"subscriber\", \"subscriber\", \"subscriber\", \"subscriber\", \"substitute\", \"substitute\", \"substitute\", \"substitute\", \"substitute\", \"substitute\", \"substitute\", \"substitute\", \"substitute\", \"success\", \"success\", \"success\", \"success\", \"success\", \"success\", \"success\", \"success\", \"success\", \"success\", \"success\", \"success\", \"success\", \"success\", \"success\", \"summit\", \"summit\", \"summit\", \"summit\", \"summit\", \"summit\", \"summit\", \"summit\", \"summit\", \"summit\", \"summit\", \"summit\", \"summit\", \"summit\", \"summit\", \"supermarket\", \"supermarket\", \"supermarket\", \"supermarket\", \"supermarket\", \"supermarket\", \"supermarket\", \"supermarket\", \"supermarket\", \"supermarket\", \"supermarket\", \"supermarket\", \"supplement\", \"supplement\", \"supplement\", \"supplement\", \"supplement\", \"supplement\", \"supplement\", \"supplement\", \"supplement\", \"supplement\", \"supplement\", \"supplement\", \"supplement\", \"supplement\", \"supreme\", \"supreme\", \"supreme\", \"supreme\", \"supreme\", \"supreme\", \"supreme\", \"supreme\", \"supreme\", \"supreme\", \"supreme\", \"supreme\", \"supreme\", \"syria\", \"syria\", \"syria\", \"syria\", \"syria\", \"syria\", \"syria\", \"syria\", \"syrian\", \"syrian\", \"syrian\", \"syrian\", \"syrian\", \"syrian\", \"syrian\", \"takeover\", \"takeover\", \"takeover\", \"takeover\", \"takeover\", \"takeover\", \"takeover\", \"takeover\", \"takeover\", \"takeover\", \"takeover\", \"takeover\", \"takeover\", \"takeover\", \"talent\", \"talent\", \"talent\", \"talent\", \"talent\", \"talent\", \"talent\", \"talent\", \"talent\", \"talent\", \"talent\", \"talent\", \"talent\", \"talent\", \"talented\", \"talented\", \"talented\", \"talented\", \"talented\", \"talented\", \"talented\", \"talented\", \"talented\", \"talented\", \"talk\", \"talk\", \"talk\", \"talk\", \"talk\", \"talk\", \"talk\", \"talk\", \"talk\", \"talk\", \"talk\", \"talk\", \"talk\", \"talk\", \"talk\", \"tangible\", \"tangible\", \"tangible\", \"tangible\", \"tangible\", \"tangible\", \"tangible\", \"tangible\", \"tangible\", \"tangible\", \"tangible\", \"tangible\", \"tariff\", \"tariff\", \"tariff\", \"tariff\", \"tariff\", \"tariff\", \"tariff\", \"tariff\", \"tariff\", \"tariff\", \"tariff\", \"tariff\", \"tariff\", \"tariff\", \"taxable\", \"taxable\", \"taxable\", \"taxable\", \"taxable\", \"taxable\", \"taxable\", \"taxable\", \"taxable\", \"taxis\", \"taxis\", \"taxis\", \"taxis\", \"taxis\", \"taxis\", \"taxis\", \"taxis\", \"taxis\", \"taxis\", \"taxis\", \"taxis\", \"taxis\", \"taxis\", \"taxis\", \"tayyip\", \"tayyip\", \"tayyip\", \"tayyip\", \"tech\", \"tech\", \"tech\", \"tech\", \"tech\", \"tech\", \"tech\", \"tech\", \"tech\", \"tech\", \"tech\", \"tech\", \"tech\", \"tech\", \"tech\", \"tehran\", \"tehran\", \"tehran\", \"tehran\", \"tehran\", \"teleconference\", \"teleconference\", \"teleconference\", \"teleconference\", \"teleconference\", \"teleconference\", \"teleconference\", \"teleconference\", \"telephone\", \"telephone\", \"telephone\", \"telephone\", \"telephone\", \"telephone\", \"telephone\", \"telephone\", \"telephone\", \"telephone\", \"telephone\", \"telephone\", \"telephone\", \"telephone\", \"telephone\", \"telephonic\", \"telephonic\", \"telephonic\", \"telephonic\", \"telephonic\", \"television\", \"television\", \"television\", \"television\", \"television\", \"television\", \"television\", \"television\", \"television\", \"television\", \"television\", \"television\", \"television\", \"television\", \"television\", \"tenant\", \"tenant\", \"tenant\", \"tenant\", \"tenant\", \"tenant\", \"tenant\", \"tenant\", \"tenant\", \"tenant\", \"tenant\", \"tenant\", \"tenant\", \"tenant\", \"tender\", \"tender\", \"tender\", \"tender\", \"tender\", \"tender\", \"tender\", \"tender\", \"tender\", \"tender\", \"tender\", \"tender\", \"tender\", \"tender\", \"tesla\", \"tesla\", \"tesla\", \"tesla\", \"tesla\", \"tesla\", \"tesla\", \"tesla\", \"tesla\", \"tesla\", \"th\", \"th\", \"th\", \"th\", \"th\", \"th\", \"th\", \"th\", \"th\", \"th\", \"th\", \"therapeutic\", \"therapeutic\", \"therapeutic\", \"therapeutic\", \"therapeutic\", \"therapeutic\", \"therapeutic\", \"therapeutic\", \"therapeutics\", \"therapeutics\", \"therapeutics\", \"therapeutics\", \"therapeutics\", \"therapeutics\", \"therapeutics\", \"therapeutics\", \"therapy\", \"therapy\", \"therapy\", \"therapy\", \"therapy\", \"therapy\", \"therapy\", \"therapy\", \"therapy\", \"therapy\", \"therapy\", \"thereof\", \"thereof\", \"thereof\", \"thereof\", \"thereof\", \"thereof\", \"theresa\", \"theresa\", \"theresa\", \"theresa\", \"theresa\", \"theresa\", \"theresa\", \"theresa\", \"theresa\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"think\", \"think\", \"think\", \"think\", \"think\", \"think\", \"think\", \"think\", \"think\", \"think\", \"think\", \"think\", \"think\", \"think\", \"think\", \"thomson\", \"thomson\", \"thomson\", \"thomson\", \"thomson\", \"thomson\", \"thomson\", \"thomson\", \"thomson\", \"thomson\", \"thomson\", \"thomson\", \"thomson\", \"thomson\", \"thomson\", \"thousand\", \"thousand\", \"thousand\", \"thousand\", \"thousand\", \"thousand\", \"thousand\", \"thousand\", \"thousand\", \"thousand\", \"thousand\", \"thousand\", \"thousand\", \"thousand\", \"thousand\", \"thrill\", \"thrill\", \"thrill\", \"thrill\", \"thrill\", \"thrill\", \"thrill\", \"thrill\", \"thrill\", \"toll\", \"toll\", \"toll\", \"toll\", \"toll\", \"toll\", \"toll\", \"toll\", \"toll\", \"toll\", \"toll\", \"toll\", \"toll\", \"toll\", \"toll\", \"tonne\", \"tonne\", \"tonne\", \"tonne\", \"tonne\", \"tonne\", \"tonne\", \"tonne\", \"tonne\", \"tonne\", \"town\", \"town\", \"town\", \"town\", \"town\", \"town\", \"town\", \"town\", \"town\", \"town\", \"town\", \"town\", \"town\", \"town\", \"town\", \"trader\", \"trader\", \"trader\", \"trader\", \"trader\", \"trader\", \"trader\", \"trader\", \"trader\", \"trader\", \"trader\", \"trader\", \"trader\", \"trader\", \"trader\", \"trading\", \"trading\", \"trading\", \"trading\", \"trading\", \"trading\", \"trading\", \"trading\", \"trading\", \"trading\", \"trading\", \"trading\", \"trading\", \"trading\", \"trading\", \"treasury\", \"treasury\", \"treasury\", \"treasury\", \"treasury\", \"treasury\", \"treasury\", \"treasury\", \"treasury\", \"treasury\", \"treasury\", \"treasury\", \"treasury\", \"treasury\", \"treasury\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"trial\", \"trial\", \"trial\", \"trial\", \"trial\", \"trial\", \"trial\", \"trial\", \"trial\", \"trial\", \"trial\", \"trial\", \"trial\", \"trial\", \"trial\", \"trump\", \"trump\", \"trump\", \"trump\", \"trump\", \"trump\", \"trump\", \"trump\", \"trump\", \"trump\", \"trump\", \"trump\", \"trump\", \"trump\", \"trump\", \"trust\", \"trust\", \"trust\", \"trust\", \"trust\", \"trust\", \"trust\", \"trust\", \"trust\", \"trust\", \"trust\", \"trust\", \"trust\", \"trust\", \"trust\", \"tsx\", \"tsx\", \"tsx\", \"tsx\", \"tsx\", \"tsx\", \"tsx\", \"tsx\", \"tsx\", \"tsx\", \"tsx\", \"tsx\", \"tumor\", \"tumor\", \"tumor\", \"turkey\", \"turkey\", \"turkey\", \"turkey\", \"turkey\", \"turkey\", \"turkey\", \"turkey\", \"turkey\", \"turkey\", \"turkey\", \"turkey\", \"turkey\", \"turkey\", \"turkey\", \"turkish\", \"turkish\", \"turkish\", \"turkish\", \"turkish\", \"turkish\", \"turkish\", \"turkish\", \"turkish\", \"tv\", \"tv\", \"tv\", \"tv\", \"tv\", \"tv\", \"tv\", \"tv\", \"tv\", \"tv\", \"tv\", \"tv\", \"tv\", \"tv\", \"uber\", \"uber\", \"uber\", \"uber\", \"uber\", \"uber\", \"uber\", \"uber\", \"uber\", \"uber\", \"uber\", \"uk\", \"uk\", \"uk\", \"uk\", \"uk\", \"uk\", \"uk\", \"uk\", \"uk\", \"uk\", \"uk\", \"uk\", \"uk\", \"uk\", \"uk\", \"ukraine\", \"ukraine\", \"ukraine\", \"ukraine\", \"ukraine\", \"ukraine\", \"ukraine\", \"ukraine\", \"ukraine\", \"ukraine\", \"ukraine\", \"unaudited\", \"unaudited\", \"unaudited\", \"unaudited\", \"unaudited\", \"unaudited\", \"unaudited\", \"unaudited\", \"unaudited\", \"unaudited\", \"unaudited\", \"unconsolidate\", \"unconsolidate\", \"underwriter\", \"underwriter\", \"underwriter\", \"underwriter\", \"underwriter\", \"underwriter\", \"underwriter\", \"underwriter\", \"underwriter\", \"underwriter\", \"underwritten\", \"underwritten\", \"underwritten\", \"underwritten\", \"underwritten\", \"underwritten\", \"underwritten\", \"undue\", \"undue\", \"undue\", \"undue\", \"undue\", \"undue\", \"undue\", \"undue\", \"undue\", \"undue\", \"union\", \"union\", \"union\", \"union\", \"union\", \"union\", \"union\", \"union\", \"union\", \"union\", \"union\", \"union\", \"union\", \"union\", \"union\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unitholder\", \"unitholder\", \"unitholder\", \"unitholder\", \"unitholder\", \"unitholder\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"unlawful\", \"unlawful\", \"unlawful\", \"unlawful\", \"unlawful\", \"unlawful\", \"unlawful\", \"unlawful\", \"unmet\", \"unmet\", \"unmet\", \"unmet\", \"unmet\", \"unparalleled\", \"unparalleled\", \"unparalleled\", \"unparalleled\", \"unparalleled\", \"unparalleled\", \"unsecured\", \"unsecured\", \"unsecured\", \"unsecured\", \"unsecured\", \"unsecured\", \"unsecured\", \"unsecured\", \"unsecured\", \"unsecured\", \"user\", \"user\", \"user\", \"user\", \"user\", \"user\", \"user\", \"user\", \"user\", \"user\", \"user\", \"user\", \"user\", \"user\", \"user\", \"validate\", \"validate\", \"validate\", \"validate\", \"validate\", \"validate\", \"validate\", \"validate\", \"validate\", \"validate\", \"validate\", \"validate\", \"vehicle\", \"vehicle\", \"vehicle\", \"vehicle\", \"vehicle\", \"vehicle\", \"vehicle\", \"vehicle\", \"vehicle\", \"vehicle\", \"vehicle\", \"vehicle\", \"vehicle\", \"vehicle\", \"vehicle\", \"versus\", \"versus\", \"versus\", \"versus\", \"versus\", \"versus\", \"versus\", \"versus\", \"versus\", \"versus\", \"versus\", \"versus\", \"versus\", \"versus\", \"versus\", \"video\", \"video\", \"video\", \"video\", \"video\", \"video\", \"video\", \"video\", \"video\", \"video\", \"video\", \"video\", \"video\", \"video\", \"video\", \"vladimir\", \"vladimir\", \"vladimir\", \"vladimir\", \"vladimir\", \"vladimir\", \"vladimir\", \"volatility\", \"volatility\", \"volatility\", \"volatility\", \"volatility\", \"volatility\", \"volatility\", \"volatility\", \"volatility\", \"volatility\", \"volatility\", \"volatility\", \"volatility\", \"volatility\", \"volkswagen\", \"volkswagen\", \"volkswagen\", \"volkswagen\", \"volkswagen\", \"volkswagen\", \"volkswagen\", \"volkswagen\", \"volkswagen\", \"volume\", \"volume\", \"volume\", \"volume\", \"volume\", \"volume\", \"volume\", \"volume\", \"volume\", \"volume\", \"volume\", \"volume\", \"volume\", \"volume\", \"volume\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"wall\", \"wall\", \"wall\", \"wall\", \"wall\", \"wall\", \"wall\", \"wall\", \"wall\", \"wall\", \"wall\", \"wall\", \"wall\", \"wall\", \"wall\", \"war\", \"war\", \"war\", \"war\", \"war\", \"war\", \"war\", \"war\", \"war\", \"war\", \"war\", \"war\", \"war\", \"war\", \"war\", \"warner\", \"warner\", \"warner\", \"warner\", \"warner\", \"warner\", \"warner\", \"warner\", \"warner\", \"warner\", \"warner\", \"warren\", \"warren\", \"warren\", \"warren\", \"warren\", \"warren\", \"warren\", \"warren\", \"warren\", \"warren\", \"warren\", \"warren\", \"warren\", \"warren\", \"washington\", \"washington\", \"washington\", \"washington\", \"washington\", \"washington\", \"washington\", \"washington\", \"washington\", \"washington\", \"washington\", \"washington\", \"washington\", \"washington\", \"washington\", \"weapon\", \"weapon\", \"weapon\", \"weapon\", \"weapon\", \"weapon\", \"weapon\", \"weapon\", \"weapon\", \"weapon\", \"weapon\", \"weapon\", \"web\", \"web\", \"web\", \"web\", \"web\", \"web\", \"web\", \"web\", \"web\", \"web\", \"web\", \"web\", \"web\", \"web\", \"webcast\", \"webcast\", \"webcast\", \"webcast\", \"webcast\", \"webcast\", \"webcast\", \"webcast\", \"webcast\", \"webcast\", \"webcast\", \"webcast\", \"webcast\", \"webcast\", \"webcast\", \"weir\", \"weir\", \"weir\", \"weir\", \"weir\", \"weir\", \"weir\", \"weir\", \"weir\", \"weir\", \"white\", \"white\", \"white\", \"white\", \"white\", \"white\", \"white\", \"white\", \"white\", \"white\", \"white\", \"white\", \"white\", \"white\", \"white\", \"wireless\", \"wireless\", \"wireless\", \"wireless\", \"wireless\", \"wireless\", \"wireless\", \"wireless\", \"wireless\", \"wireless\", \"wireless\", \"wireless\", \"wireless\", \"wireless\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"wong\", \"wong\", \"wong\", \"wong\", \"wong\", \"wong\", \"wong\", \"wong\", \"wong\", \"wong\", \"wong\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"yemen\", \"yemen\", \"yemen\", \"yemen\", \"yemen\", \"yemen\", \"yemen\", \"yen\", \"yen\", \"yen\", \"yen\", \"yen\", \"yen\", \"yen\", \"yen\", \"yen\", \"yen\", \"yen\", \"yen\", \"yen\", \"yield\", \"yield\", \"yield\", \"yield\", \"yield\", \"yield\", \"yield\", \"yield\", \"yield\", \"yield\", \"yield\", \"yield\", \"yield\", \"yield\", \"yield\", \"yuan\", \"yuan\", \"yuan\", \"yuan\", \"yuan\", \"yuan\", \"yuan\", \"yuan\", \"yuan\", \"yuan\", \"yuan\", \"yuan\", \"zuckerberg\", \"zuckerberg\", \"zuckerberg\", \"zuckerberg\", \"zuckerberg\", \"zuckerberg\", \"zuckerberg\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [7, 3, 2, 13, 15, 1, 12, 11, 6, 14, 10, 4, 5, 8, 9]};\n",
       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el155951401673927676962195886734\", ldavis_el155951401673927676962195886734_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el155951401673927676962195886734\", ldavis_el155951401673927676962195886734_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el155951401673927676962195886734\", ldavis_el155951401673927676962195886734_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vis = prepare(lda_models[15], corpus, dictionary, mds='tsne')\n",
    "pyLDAvis.display(vis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LDAMultiCore Timing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T04:23:43.680623Z",
     "start_time": "2020-06-21T04:23:43.664986Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>workers</th>\n",
       "      <th>num_topics</th>\n",
       "      <th>duration</th>\n",
       "      <th>test_perplexity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2035.206371</td>\n",
       "      <td>72.621968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>1611.578738</td>\n",
       "      <td>84.457663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>1093.389884</td>\n",
       "      <td>72.742675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>50</td>\n",
       "      <td>1067.982344</td>\n",
       "      <td>82.115884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>1154.867700</td>\n",
       "      <td>72.791885</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   workers  num_topics     duration  test_perplexity\n",
       "0        1          10  2035.206371        72.621968\n",
       "1        1          50  1611.578738        84.457663\n",
       "2        4          10  1093.389884        72.742675\n",
       "3        4          50  1067.982344        82.115884\n",
       "4        8          10  1154.867700        72.791885"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(results_path / 'lda_multicore_test_results.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T11:54:36.393837Z",
     "start_time": "2020-06-21T11:54:36.038349Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jiRMnqra2VuPHj1e3bt3kcDgUHh4uSTKbzaqsrGyGIQAAAAAA0Prd0HfQ/9n27ds1dOhQ+fv7S5JCQ0M1fvx492Xwffv2VXl5ucLCwuR0OiVJTqdTERERTRQbAAAAAADv0qi7uBcXFysuLs69fOLECVmtVtXW1qqmpkb79+9X165d1aNHD+3Zs0eSVFhYqJ49ezZNagAAAAAAvEyjzqAfP35cd911l3v5nnvuUWJiopKSkhQYGKhhw4apc+fOioyMVFpamiwWiwIDA7V06dImCw4AAAAAgDe5oYIeGRmp/Px89/LWrVsbbDN58mRNnjy53rrQ0FAtX778FiMCAAAAAOD9GnWJOwAAAAAAaFoUdAAAAAAADICCDgAAAACAATTqJnEAAKD1e/vtt/WnP/1JNTU1slgs6t27t2bNmiWTyaTOnTsrIyNDfn58lg8AQEth1gUAwAeVlJTor3/9q9avXy+73a7vvvtOCxcuVGpqqnJzc+VyubRz505PxwQAwKdQ0AEA8EGffvqpYmJiNHXqVE2ZMkUDBw5UWVmZevfuLUmKi4tTUVGRh1MCAOBbuMQdAAAfdP78eX3zzTdauXKlKioqlJKSIpfLJZPJJEkym82qrKz0cEoAAHwLBR0AAB/Upk0bRUdHKygoSNHR0QoODtZ3333nft3pdCoiIsKDCQEA8D1c4g4AgA/q2bOnPvnkE7lcLp0+fVqXLl3SQw89pJKSEklSYWGhevXq5eGUAAD4Fs6gAwDggwYNGqR9+/Zp9OjRcrlcmjdvniIjIzV37lxlZWUpOjpaCQkJno4JAIBPoaADAOCjXn755Qbr1q5d64EkAABA4hJ3AAAAAAAMgYIOAAAAAIABUNABAAAAADAACjoAAAAAAAZAQQcAAAAAwAAo6AAAAAAAGAAFHQAAAAAAA6CgAwAAAABgABR0AAAAAAAMgIIOAAAAAIABUNABAAAAADAACjoAAAAAAAZAQQcAAAAAwAAo6AAAAAAAGAAFHQAAAAAAA7ihgn7gwAHZbDZJUllZmfr37y+bzSabzaZt27ZJkvLz8zVy5EglJSVp165dkqTLly9r2rRpslqtmjx5ss6dO9dMwwAAAAAAoHULuN4Gq1evVkFBgUJDQyVJhw8f1sSJE5WcnOze5syZM7Lb7dq0aZOqqqpktVrVr18/rV+/XjExMZo2bZq2bt2qnJwcpaenN99oAAAAAABopa57Bj0qKkrZ2dnu5UOHDmn37t0aN26c5syZI4fDoYMHD6p79+4KCgpSeHi4oqKiVF5ertLSUvXv31+SFBcXp+Li4uYbCQAAAAAArdh1C3pCQoICAv5xoj02NlYvv/yy1q1bp7vuuksrVqyQw+FQeHi4exuz2SyHw1FvvdlsVmVlZTMMAQAAAACA1u+mbxIXHx+vbt26uX8+fPiwwsLC5HQ63ds4nU6Fh4fXW+90OhUREdFEsQEAAAAA8C43XdAnTZqkgwcPSpKKi4vVtWtXxcbGqrS0VFVVVaqsrNSxY8cUExOjHj16aM+ePZKkwsJC9ezZs2nTAwAAAADgJa57k7h/Nn/+fGVmZiowMFDt2rVTZmamwsLCZLPZZLVa5XK5NGPGDAUHB8tisSgtLU0Wi0WBgYFaunRpc4wBAAAAAIBW74YKemRkpPLz8yVJXbt2VV5eXoNtkpKSlJSUVG9daGioli9f3gQxAQAAAADwbjd9iTsAAAAAAGh6FHQAAAAAAAyAgg4AAAAAgAFQ0AEAAAAAMAAKOgAAAAAABkBBBwAAAADAACjoAAAAAAAYwA09Bx0AAHif4cOHKzw8XJIUGRmpKVOmaNasWTKZTOrcubMyMjLk58dn+QAAtBQKOgAAPqiqqkqSZLfb3eumTJmi1NRU9enTR/PmzdPOnTsVHx/vqYgAAPgcPhYHAMAHlZeX69KlS0pOTtb48eP1+eefq6ysTL1795YkxcXFqaioyMMpAQDwLZxBBwDAB4WEhGjSpEl6+umndeLECU2ePFkul0smk0mSZDabVVlZ6eGUAAD4Fgo6AAA+qGPHjurQoYNMJpM6duyoNm3aqKyszP260+lURESEBxMCAOB7uMQdAAAftHHjRi1atEiSdPr0aTkcDvXr108lJSWSpMLCQvXq1cuTEQEA8DmcQQcAwAeNHj1as2fPlsVikclk0oIFC3T77bdr7ty5ysrKUnR0tBISEjwdEwAAn0JBBwDABwUFBWnp0qUN1q9du9YDaQAAgMQl7gAAAAAAGAIFHQAAAAAAA6CgAwAAAABgABR0AECLulxT6+kILcJXxgkAAJoON4kDALSokEB/3T1rq6djNLsTi570dAQAANDKcAYdAAAAAAADoKADAAAAAGAAFHQAAAAAAAyAgg4AAAAAgAFQ0AEAAAAAMAAKOgAAAAAABnBDj1k7cOCAXn/9ddntdn355ZfKzMyUv7+/goKCtHjxYrVr106vvvqq9u/fL7PZLEnKyclRYGCgZs6cqbNnz8psNmvx4sVq27Ztsw4IAAAAAIDW6Lpn0FevXq309HRVVVVJkl577TXNnTtXdrtd8fHxWr16tSSprKxM77zzjux2u+x2u8LDw7V+/XrFxMQoNzdXw4cPV05OTvOOBgAAAACAVuq6BT0qKkrZ2dnu5aysLP3617+WJNXW1io4OFh1dXU6efKk5s2bp7Fjx2rjxo2SpNLSUvXv31+SFBcXp+Li4uYYAwAAAAAArd51L3FPSEhQRUWFe7l9+/aSpP3792vt2rVat26dfvjhBz3zzDOaOHGiamtrNX78eHXr1k0Oh0Ph4eGSJLPZrMrKymYaBgAAAAAArdsNfQf9n23btk1vvfWWVq1apbZt27pLeWhoqCSpb9++Ki8vV1hYmJxOpyTJ6XQqIiKi6ZIDAAAAAOBFbvou7lu2bNHatWtlt9t11113SZJOnDghq9Wq2tpa1dTUaP/+/eratat69OihPXv2SJIKCwvVs2fPpk0PAAAAAICXuKkz6LW1tXrttdd05513atq0aZKkBx98UNOnT1diYqKSkpIUGBioYcOGqXPnzoqMjFRaWposFosCAwO1dOnSZhkEAAAAAACt3Q0V9MjISOXn50uSPvvss2tuM3nyZE2ePLneutDQUC1fvvwWIwIAAAAA4P1u+hJ3AAAAAADQ9CjoAAAAAAAYAAUdAAAAAAADoKADAAAAAGAAFHQAAAAAAAyAgg4AAAAAgAFQ0AEAAAAAMAAKOgAAAAAABkBBB2B4l2tqPR2hRfjKOAEAAHBtAZ4OAADXExLor7tnbfV0jGZ3YtGTno4AAAAAD+IMOgAAAAAABkBBBwDAh509e1YDBgzQsWPHdPLkSVksFlmtVmVkZKiurs7T8QAA8CkUdAAAfFRNTY3mzZunkJAQSdLChQuVmpqq3NxcuVwu7dy508MJAQDwLRR0AAB81OLFizV27Fi1b99eklRWVqbevXtLkuLi4lRUVOTJeAAA+BwKOgAAPmjz5s1q27at+vfv717ncrlkMpkkSWazWZWVlZ6KBwCAT+Iu7gAA+KBNmzbJZDKpuLhYX375pdLS0nTu3Dn3606nUxERER5MCACA76GgAwDgg9atW+f+2Wazaf78+VqyZIlKSkrUp08fFRYWqm/fvh5MCACA7+ESd3ilyzW1no7QInxlnABaRlpamrKzszVmzBjV1NQoISHB05EAAPApnEGHVwoJ9Nfds7Z6OkazO7HoSU9HAOAF7Ha7++e1a9d6MAkAAL6NM+gAAAAAABgABR0AAAAAAAOgoAMAAAAAYAAUdAAAAAAADICCDgAAAACAAVDQAQAAAAAwAAo6AAAAAAAGQEEHAAAAAMAAbqigHzhwQDabTZJ08uRJWSwWWa1WZWRkqK6uTpKUn5+vkSNHKikpSbt27ZIkXb58WdOmTZPVatXkyZN17ty5ZhoGAAAAAACt23UL+urVq5Wenq6qqipJ0sKFC5Wamqrc3Fy5XC7t3LlTZ86ckd1uV15entasWaOsrCxVV1dr/fr1iomJUW5uroYPH66cnJxmHxAAAAAAAK3RdQt6VFSUsrOz3ctlZWXq3bu3JCkuLk5FRUU6ePCgunfvrqCgIIWHhysqKkrl5eUqLS1V//793dsWFxc30zAAAAAAAGjdrlvQExISFBAQ4F52uVwymUySJLPZrMrKSjkcDoWHh7u3MZvNcjgc9dZf3RYAAAAAADR00zeJ8/P7xy5Op1MREREKCwuT0+mstz48PLze+qvbAgAAAACAhm66oN93330qKSmRJBUWFqpXr16KjY1VaWmpqqqqVFlZqWPHjikmJkY9evTQnj173Nv27NmzadMDAAAAAOAlAq6/SX1paWmaO3eusrKyFB0drYSEBPn7+8tms8lqtcrlcmnGjBkKDg6WxWJRWlqaLBaLAgMDtXTp0uYYAwAAAAAArd4NFfTIyEjl5+dLkjp27Ki1a9c22CYpKUlJSUn11oWGhmr58uVNEBMAAAAAAO9205e4AwAAAACApkdBBwAAAADAACjoAAAAAAAYAAUdAAAAAAADoKADAAAAAGAAFHQAAAAAAAyAgg4AAAAAgAFQ0AEAAAAAMAAKOgAAAAAABkBBBwAAAADAACjoAAAAAAAYAAUdAAAAAAADoKADAAAAAGAAFHQAAAAAAAwgwNMBAABAy6utrVV6erqOHz8uf39/LVy4UC6XS7NmzZLJZFLnzp2VkZEhPz8+ywcAoKVQ0AEA8EG7du2SJOXl5amkpMRd0FNTU9WnTx/NmzdPO3fuVHx8vIeTAgDgO/hYHAAAH/TYY48pMzNTkvTNN9+oXbt2KisrU+/evSVJcXFxKioq8mREAAB8DgUdAAAfFRAQoLS0NGVmZiohIUEul0smk0mSZDabVVlZ6eGEAAD4Fgo6AAA+bPHixfqv//ovzZ07V1VVVe71TqdTERERHkwGAIDvoaADAOCDPvzwQ7399tuSpNDQUJlMJnXr1k0lJSWSpMLCQvXq1cuTEQEA8DncJA4AAB/0+OOPa/bs2Ro3bpyuXLmiOXPm6J577tHcuXOVlZWl6OhoJSQkeDomAAA+hYIOAIAPuu2227Rs2bIG69euXeuBNAAAQOISdwAAAAAADIGCDgAAAACAAVDQAQAAAAAwAAo6AAAAAAAG0KibxG3evFkffPCBJKmqqkpffvml8vLyNGXKFN19992SJIvFoiFDhig/P195eXkKCAhQSkqKBg0a1GThAQAAAADwFo0q6CNHjtTIkSMlSf/xH/+hUaNG6fDhw5o4caKSk5Pd2505c0Z2u12bNm1SVVWVrFar+vXrp6CgoKZJDwAAAACAl7ilS9y/+OILHT16VGPGjNGhQ4e0e/dujRs3TnPmzJHD4dDBgwfVvXt3BQUFKTw8XFFRUSovL2+q7AAAAAAAeI1bKuhvv/22pk6dKkmKjY3Vyy+/rHXr1umuu+7SihUr5HA4FB4e7t7ebDbL4XDcWmIAAAAAALxQowv6xYsX9fXXX6tv376SpPj4eHXr1s398+HDhxUWFian0+nex+l01ivsAAAAAADg7xpd0Pft26eHH37YvTxp0iQdPHhQklRcXKyuXbsqNjZWpaWlqqqqUmVlpY4dO6aYmJhbTw0AAAAAgJdp1E3iJOn48eOKjIx0L8+fP1+ZmZkKDAxUu3btlJmZqbCwMNlsNlmtVrlcLs2YMUPBwcFNEhwAAAAAAG/S6IL+7LPP1lvu2rWr8vLyGmyXlJSkpKSkxh4GAAAAAACfcEs3iQMAAAAAAE2Dgg4AAAAAgAFQ0AEAAAAAMAAKOgAAAAAABkBBBwAAAADAACjoAAAAAAAYAAUdAAAAAAADoKADAAAAAGAAFHQAAAAAAAyAgg4AAAAAgAFQ0AEAAAAAMAAKOgAAAAAABkBBBwAAAADAACjoAAAAAAAYAAUdAAAAAAADoKADAAAAAGAAFHQAAAAAAAyAgg4AAAAAgAEEeDoAAABoeTU1NZozZ45OnTql6upqpaSkqFOnTpo1a5ZMJpM6d+6sjIwM+fnxWT4AAC2Fgg4AgA8qKChQmzZttGTJEp0/f14jRoxQly5dlJqaqj59+mjevHnauXOn4uPjPR0VAACfwcfiAAD4oMGDB+uFF15wL/v7+6usrEy9e6EnCmIAAA3BSURBVPeWJMXFxamoqMhT8QAA8EkUdAAAfJDZbFZYWJgcDoemT5+u1NRUuVwumUwm9+uVlZUeTgkAgG+hoAMA4KO+/fZbjR8/XsOGDVNiYmK975s7nU5FRER4MB0AAL6Hgg4AgA/6/vvvlZycrJkzZ2r06NGSpPvuu08lJSWSpMLCQvXq1cuTEQEA8DkUdAAAfNDKlSt18eJF5eTkyGazyWazKTU1VdnZ2RozZoxqamqUkJDg6ZgAAPgU7uIOAIAPSk9PV3p6eoP1a9eu9UAaAAAg3UJBHz58uMLDwyVJkZGRmjJlyjWfnZqfn6+8vDwFBAQoJSVFgwYNarLwAAAAAAB4i0YV9KqqKkmS3W53r5syZUqDZ6c+8MADstvt2rRpk6qqqmS1WtWvXz8FBQU1TXoAAAAAALxEowp6eXm5Ll26pOTkZF25ckX//u//3uDZqXv37pWfn5+6d++uoKAgBQUFKSoqSuXl5YqNjW3SQQAAAAAA0No1qqCHhIRo0qRJevrpp3XixAlNnjz5ms9OdTgc7svgr653OBxNkxwAAAAAAC/SqILesWNHdejQQSaTSR07dlSbNm1UVlbmfv3qs1PDwsLkdDrrrf9xYQcAAAAAAH/XqMesbdy4UYsWLZIknT59Wg6HQ/369Wvw7NTY2FiVlpaqqqpKlZWVOnbsmGJiYpouPQAAAAAAXqJRZ9BHjx6t2bNny2KxyGQyacGCBbr99ts1d+5cZWVlKTo6WgkJCfL395fNZpPVapXL5dKMGTMUHBzc1GMAAAAAAKDVa1RBDwoK0tKlSxusv9azU5OSkpSUlNSYwwAAAAAA4DMadYk7AAAAAABoWhR0AAAAAAAMgIIOAAAAAIABUNABAAAAADAACjoAAAAAAAZAQQcAAAAAwAAo6AAAAAAAGAAFHQAAAAAAA6CgAwAAAABgABR0AAAAAAAMgIIOAAAAAIABUNABAAAAADAACjoAAAAAAAZAQQcAAAAAwAAo6AAAAAAAGAAFHQAAAAAAA6CgAwAAAABgABR0AAAAAAAMgIIOAAAAAIABUNABAAAAADAACjoAAAAAAAZAQQcAAAAAwAAo6AAAAAAAGAAFHQAAH3bgwAHZbDZJ0smTJ2WxWGS1WpWRkaG6ujoPpwMAwLdQ0AEA8FGrV69Wenq6qqqqJEkLFy5UamqqcnNz5XK5tHPnTg8nBADAt1DQAQDwUVFRUcrOznYvl5WVqXfv3pKkuLg4FRUVeSoaAAA+iYIOAICPSkhIUEBAgHvZ5XLJZDJJksxmsyorKz0VDQAAnxRw/U0aqqmp0Zw5c3Tq1ClVV1crJSVFd9xxh6ZMmaK7775bkmSxWDRkyBDl5+crLy9PAQEBSklJ0aBBg5oyPwAAaCJ+fv/43N7pdCoiIsKDaQAA8D2NKugFBQVq06aNlixZovPnz2vEiBGaOnWqJk6cqOTkZPd2Z86ckd1u16ZNm1RVVSWr1ap+/fopKCioyQYAAACaxn333aeSkhL16dNHhYWF6tu3r6cjAQDgUxp1ifvgwYP1wgsvuJf9/f116NAh7d69W+PGjdOcOXPkcDh08OBBde/eXUFBQQoPD1dUVJTKy8ubLDwAAGg6aWlpys7O1pgxY1RTU6OEhARPRwIAwKc06gy62WyWJDkcDk2fPl2pqamqrq7W008/rW7duumtt97SihUr1KVLF4WHh9fbz+FwNE1yAABwyyIjI5Wfny9J6tixo9auXevhRAAA+K5G3yTu22+/1fjx4zVs2DAlJiYqPj5e3bp1kyTFx8fr8OHDCgsLk9PpdO/jdDrrFXYAAAAAAPB3jSro33//vZKTkzVz5kyNHj1akjRp0iQdPHhQklRcXKyuXbsqNjZWpaWlqqqqUmVlpY4dO6aYmJimSw8AAAAAgJdo1CXuK1eu1MWLF5WTk6OcnBxJ0qxZs7RgwQIFBgaqXbt2yszMVFhYmGw2m6xWq1wul2bMmKHg4OAmHQAAAAAAAN6gUQU9PT1d6enpDdbn5eU1WJeUlKSkpKTGHAYAAAAAAJ/R6O+gAwAAAACApkNBBwAAAADAACjoAAAAAAAYAAUdAAAAAAADoKADAAAAAGAAFHQAAAAAAAyAgg4AAAAAgAFQ0AEAAAAAMAAKOgAAAAAABkBBBwAAAADAACjoAAAAAAAYAAUdAAAAAAADoKADAAAAAGAAFHQAAAAAAAyAgg4AAAAAgAFQ0AEAAAAAMAAKOgAAAAAABkBBBwAAAADAACjoAAAAAAAYAAUdAAAAAAADoKADAAAAAGAAFHQAAAAAAAyAgg4AAAAAgAFQ0AEAAAAAMAAKOgAAAAAABkBBBwAAAADAAAKa+wB1dXWaP3++jhw5oqCgIL366qvq0KFDcx8WAADcJOZsAAA8q9nPoO/YsUPV1dXasGGDXnzxRS1atKi5DwkAABqBORsAAM9q9oJeWlqq/v37S5IeeOABHTp0qLkPCQAAGoE5GwAAzzK5XC5Xcx7glVde0eOPP64BAwZIkgYOHKgdO3YoIOCnr67v06ePfvWrXzVnLAAAvM7tt9+uNWvWNHp/5mwAAFrGT83Zzf4d9LCwMDmdTvdyXV3dz070klRSUtLcsQAAwD9hzgYAwLOa/RL3Hj16qLCwUJL0+eefKyYmprkPCQAAGoE5GwAAz2r2S9yv3hH2q6++ksvl0oIFC3TPPfc05yEBAEAjMGcDAOBZzV7QAQAAAADA9TX7Je4AAAAAAOD6KOgAAAAAABgABR0AAAAAAAOgoAO36OzZs56OgCZWXV2ty5cvezoGAKCJMWd7H+ZseBsKOnCTjh8/Xu9PSkqK+2e0TsePH9f06dP14osv6vPPP1diYqKefPJJbdu2zdPRAAC3gDnb+zBnw9txF3fgJg0cOFAhISFq3769XC6XysvL1aVLF5lMJr333nuejodGeOaZZ/Tcc8+psrJS8+bNU0FBgcLDwzVx4kRt2LDB0/EAAI3EnO19mLPh7QI8HcBX2Gw21dTU1FvncrlkMpmUl5fnoVRojE2bNikjI0MWi0X9+vWTzWaT3W73dCzcgitXrujhhx+Wy+VSVlaWfvnLX0qSAgL4J7I1+p//+R99/fXX6tOnj1atWqWysjJ16tRJU6ZMUXh4uKfjoRVgzvYezNnehznbuzBnN8QZ9BZy4MABpaena8WKFfL396/32q9+9SsPpUJjXblyRYsXL9a//Mu/aO/evUz2rdyLL76ouro61dbWqqKiQv3791dYWJjKysr05ptvejoebpLVatULL7ygP/zhD7rjjjv06KOPat++ffr000+1atUqT8dDK8Cc7V2Ys70Lc7Z3Yc5uiI+aWsj999+vYcOG6ciRI4qPj/d0HNyigIAAvfLKK9q8ebP4jKv1W7x4sfbs2aO7775bZrNZ//mf/6mQkBAtWLDA09HQCP7+/urTp49WrlypzMxMSdKvf/1rffTRRx5OhtaCOdu7MGd7F+Zs78Kc3RBn0AEAXuW5557TU089pe+++05t2rTRoEGDtGfPHn344Yd69913PR0PAAD8P+bshijoAACvcu7cOS1ZskT79+/XqVOn1KZNG/Xs2VNpaWn613/9V0/HAwAA/485uyEKOgAAAAAABsB30AEAXuVad+C+ijtwAwBgHMzZDXEGHQDgVbgDNwAArQNzdkMUdACA13nnnXfUoUMH7sANAIDBMWfXR0EHAAAAAMAA/DwdAAAAAAAAUNABAAAAADAECjqA66qoqFBSUpKnYwAAgOtgzgZaNwo6AAAAAAAGQEEHfMSIESN09uxZ1dTUqEePHjp8+LB7/apVqzRq1CiNGTNGS5YskSRlZ2crOTlZY8eOVVVVlSSptrZWM2fO1KpVqyRJdrtdY8aM0dixY/Xee+9JkmbNmqUpU6Zo7NixOnfunMaPHy+bzaaxY8fqyJEjHhg5AACtC3M24LsCPB0AQMv4zW9+o08++UR33HGHIiMjtXfvXgUFBSkyMlIff/yx8vLyFBAQoGnTpmnXrl2SpOjoaKWnp6uiokJXrlzRSy+9pF69emncuHE6evSotm3bptzcXJlMJk2YMEGPPPKIJKlv376aMGGCdu/erfDwcC1dulRHjx6Vw+Hw5K8AAIBWgTkb8F0UdMBHPP7441q5cqXuvPNOzZgxQ3a7XS6XS0OGDFFpaakCAwMlSb169dLf/vY3SVLHjh3d+x85ckRhYWH64YcfJElfffWVvvnmG02YMEGSdOHCBf33f/93vf3i4uJ04sQJPffccwoICFBKSkpLDRcAgFaLORvwXVziDviImJgYVVRU6ODBgxowYIB++OEH7dy5U9HR0Tp48KCuXLkil8ulffv2uSdrP79//BPRtWtXrVq1SgUFBSovL1d0dLQ6deqk9957T3a7XSNHjlRMTIwkyWQySZJKSkrUvn17vfvuu0pJSVFWVlbLDxwAgFaGORvwXZxBB3zIgw8+qIqKCvn5+enBBx/U0aNHde+99+qJJ56QxWJRXV2devbsqccee0zl5eUN9g8JCdH8+fOVlpam999/Xw899JAsFouqq6sVGxurX/7yl/W279Kli2bMmKHf//738vPz09SpU1tqqAAAtGrM2YBvMrlcLpenQwAAAAAA4Ou4xB0AAAAAAAOgoAMAAAAAYAAUdAAAAAAADICCDgAAAACAAVDQAQAAAAAwAAo6AAAAAAAGQEEHAAAAAMAA/g/lUpcSaR2X5wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df.num_topics==10].set_index('workers')[['duration', 'test_perplexity']].plot.bar(subplots=True, layout=(1,2), figsize=(14,5), legend=False)\n",
    "sns.despine()\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "name": "_merged",
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "387.145px",
    "left": "1219px",
    "right": "1064px",
    "top": "29px",
    "width": "234.312px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
